Automated Analysis of the Reproductive Response of Two Panamanian Forests to ENSO Climate Variation - A Machine Learning Pilot Study

包含在化石花粉和孢子记录是植被及其对长期环境变化的反应最全面的历史之一。然而，科学家们一直无法有效地利用这一记录，因为研究花粉的方式仍然与一个世纪前一样，技术含量很低：由训练有素的专家使用透射光显微镜。本研究旨在通过开发一种能够识别和分类极其多样化的花粉和孢子样本的自动化系统，将花粉和孢子的研究转变为一门高通量和精确的科学。该系统将建立自动分类的标准方法，并基于机器学习和计算机视觉的最新进展。该系统将在17年来从巴拿马两个热带森林的花粉陷阱中收集的900个花粉材料样本上进行开发和测试。自动化将首次使人们能够详细研究花粉的季节性生产对多次厄尔尼诺-南方涛动（ENSO）事件的响应。本研究结果将提高热带花粉数据的数量和质量，促进对化石花粉记录中所记录的植物群落大尺度动态的必要研究。这项研究代表了科学家对花粉数据分析的思考和处理方式的根本转变。即将开发的机器学习软件将向公众开放，希望其他研究人员能够采用这些方法和标准，并建立客观的措施，以实现一致和可靠的花粉鉴定。该项目的教育目标包括为代表性不足的第一代大学生提供本科研究培训。

Contained within the fossil pollen and spore record is one of the most comprehensive histories of vegetation and its response to long term environmental change. However, scientists have been unable to efficiently use this record because pollen is still studied much in the same low technology way it was a century ago: by a highly trained expert using a transmitted light microscope. The proposed research intends to transform the study of pollen and spores into a high throughput and precise science by developing an automated system capable of identifying and classifying extremely diverse pollen and spore samples. This system will establish standards approaches for automated classification and be based on recent advances in machine learning and computer vision. The system will be developed and tested on 900 samples of pollen material collected over seventeen years from pollen traps placed in two tropical forests in Panama. Automation will allow, for the first time, a detailed study of the seasonal production of pollen in response to multiple El Nino-Southern Oscillation, or ENSO, events.The results of the proposed research will increase the quantity and quality of tropical pollen data and promote needed research on the large scale dynamics of plant communities that is recorded in fossil pollen records. This research represents a fundamental transformation in the way scientists think about and approach the analysis of pollen data. The machine learning software that will be developed will be publically available, with the hope that other researchers will then adopt the methods and standards and establish objective measures for consistent and reliable pollen identifications. Educational goals of this project include undergraduate research training for underrepresented and first generation college students.